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Posted to issues@spark.apache.org by "Michael Armbrust (JIRA)" <ji...@apache.org> on 2015/05/20 21:33:59 UTC
[jira] [Updated] (SPARK-7178) Improve DataFrame documentation and
code samples
[ https://issues.apache.org/jira/browse/SPARK-7178?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Michael Armbrust updated SPARK-7178:
------------------------------------
Target Version/s: 1.5.0 (was: 1.4.0)
> Improve DataFrame documentation and code samples
> ------------------------------------------------
>
> Key: SPARK-7178
> URL: https://issues.apache.org/jira/browse/SPARK-7178
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 1.3.1
> Reporter: Chris Fregly
> Labels: dataframe
>
> AND and OR are not straightforward when using the new DataFrame API.
> the current convention - accepted by Pandas users - is to use the bitwise & and | instead of AND and OR. when using these, however, you need to wrap each expression in parenthesis to prevent the bitwise operator from dominating.
> also, working with StructTypes is a bit confusing. the following link: https://spark.apache.org/docs/latest/sql-programming-guide.html#programmatically-specifying-the-schema (Python tab) implies that you can work with tuples directly when creating a DataFrame.
> however, the following code errors out unless we explicitly use Row's:
> {code}
> from pyspark.sql import Row
> from pyspark.sql.types import *
> # The schema is encoded in a string.
> schemaString = "a"
> fields = [StructField(field_name, MapType(StringType(),IntegerType())) for field_name in schemaString.split()]
> schema = StructType(fields)
> df = sqlContext.createDataFrame([Row(a={'b': 1})], schema)
> {code}
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